Python Package for Mechanical Laboratory Testing
This package is used for calculating the A-basis and B-Basis mechanical properties from mechanical laboratory test results and data.
An example from CMH-17 is shown below.
Input file 'cmh17_pg_8-87.json':
{
"Material": "Graphite/Epoxy",
"Property": "Compression Strength",
"Test Environment": "ETW",
"Program": "Qualification Data",
"batches": {
"1": {
"datasets": {
"1": {
"coupons": {
"1": 106.358,
"2": 105.899,
"3": 88.464,
"4": 103.902,
"5": 80.206,
"6": 109.2,
"7": 61.014
}
}
}
},
"2": {
"datasets": {
"2": {
"coupons": {
"1": 99.321,
"2": 115.862,
"3": 82.613,
"4": 85.369,
"5": 115.802,
"6": 44.322,
"7": 117.328,
"8": 88.678
}
}
}
},
"3": {
"datasets": {
"3": {
"coupons": {
"1": 107.677,
"2": 108.960,
"3": 116.123,
"4": 80.233,
"5": 106.146,
"6": 104.668,
"7": 104.235
}
}
}
}
}
}
Python script 'cmh17_script.py':
#%%
# Import Dependencies
from IPython.display import display_markdown
from pymechlab.classes.cmh17statistics import pool_from_json
#%%
# Import JSON File
jsonfilepath = '../files/cmh17_pg_8-87.json'
pool = pool_from_json(jsonfilepath)
display_markdown(pool)
#%%
# Import JSON File
jsonfilepath = '../files/cmh17_pg_8-92.json'
pool = pool_from_json(jsonfilepath)
display_markdown(pool)
The output results in markdown are as follows:
| Pool Information |
Value |
| Material |
Graphite/Epoxy |
| Property |
Compression Strength |
| Test Environment |
ETW |
| Program |
Qualification Data |
| Number of Specimens |
22 |
| Number of Batches |
3 |
| Number of Data Sets |
3 |
| Minimum Value |
44.322 |
| Maximum Value |
117.328 |
| Batch ID |
Data Set ID |
Coupon ID |
Data Values |
Before Pooling |
After Pooling |
| 1 |
1 |
1 |
106.358 |
|
|
| 1 |
1 |
2 |
105.899 |
|
|
| 1 |
1 |
3 |
88.464 |
|
|
| 1 |
1 |
4 |
103.902 |
|
|
| 1 |
1 |
5 |
80.206 |
|
|
| 1 |
1 |
6 |
109.2 |
|
|
| 1 |
1 |
7 |
61.014 |
|
|
| 2 |
2 |
1 |
99.321 |
|
|
| 2 |
2 |
2 |
115.862 |
|
|
| 2 |
2 |
3 |
82.613 |
|
|
| 2 |
2 |
4 |
85.369 |
|
|
| 2 |
2 |
5 |
115.802 |
|
|
| 2 |
2 |
6 |
44.322 |
|
X |
| 2 |
2 |
7 |
117.328 |
|
|
| 2 |
2 |
8 |
88.678 |
|
|
| 3 |
3 |
1 |
107.677 |
|
|
| 3 |
3 |
2 |
108.96 |
|
|
| 3 |
3 |
3 |
116.123 |
|
|
| 3 |
3 |
4 |
80.233 |
X |
|
| 3 |
3 |
5 |
106.146 |
|
|
| 3 |
3 |
6 |
104.668 |
|
|
| 3 |
3 |
7 |
104.235 |
|
|
| Anderson k-Sample Test |
Value |
| ADK Statistic |
0.328731 |
| AD Critical (α = 0.250) |
0.449259 |
| AD Critical (α = 0.100) |
1.30528 |
| AD Critical (α = 0.050) |
1.94342 |
| AD Critical (α = 0.025) |
2.57697 |
| AD Critical (α = 0.010) |
3.41635 |
| AD Critical (α = 0.005) |
4.0721 |
| AD Critical (α = 0.001) |
5.56419 |
| ADK p-value |
0.2888 |
| Normal Distribution Statistics |
Value |
| Observed Significance Level (OSL) |
0.00605107 |
| Mean |
96.9264 |
| Standard Deviation |
18.8048 |
| Coefficient of Variation (%) |
19.4012% |
| B-Basis Value |
61.4527 |
| A-Basis Value |
36.1265 |
| Log Normal Distribution Statistics |
Value |
| Observed Significance Level (OSL) |
0.000307372 |
| Log Mean |
4.55097 |
| Log Standard Deviation |
0.234756 |
| B-Basis Value |
60.8328 |
| A-Basis Value |
44.3433 |
| Two Param Weibull Distribution Statistics |
Value |
| Observed Significance Level (OSL) |
0.0218837 |
| Scale Parameter |
103.847 |
| Shape Parameter |
7.28576 |
| B-Basis Value |
66.864 |
| A-Basis Value |
43.1806 |
| Non-Parametric Statistics |
Value |
| B-Basis Method |
Hans-Koop |
| A-Basis Method |
Hans-Koop |
| B-Basis Rank |
10 |
| A-Basis Rank |
N/A |
| B-Basis Hans-Koop k Factor |
1.18418 |
| A-Basis Hans-Koop k Factor |
2.2602 |
| B-Basis Value |
37.8853 |
| A-Basis Value |
12.9966 |
| Parameter |
Value |
| Fcalc |
1.50529 |
| pcalc |
0.247263 |
| Fcrit |
4.67443 |
| ANOVA Statistics |
Value |
| Sample Between-batch Mean Sq. (MSB) |
257.302 |
| Error Mean Square (MSE) |
363.761 |
| Estimate of Pop. Std. Deviation(s) |
18.6873 |
| B-Basis Tolerance Limit Factor (TB) |
1.88641 |
| A-Basis Tolerance Limit Factor (TA) |
3.2332 |
| B-Basis Value |
61.6745 |
| A-Basis Value |
36.5067 |
